Recently, deep learning algorithms, e.g., Convolutional Neural Networks (CNNs) [1]}, have been widely used to detect lung diseases via CT images. For example, researchers applied existing CNN frameworks, such as U-Net [2]}, to the automatic segmentation of COVID-19 CT scans [3]}, [4]}, [5]}. To achieve satisfactory results, the highly accurate annotation of lesions is essential. However, obtaining a large amount of annotation of infections is expensive and time-consuming.
COPLE-Net was designed to enhance the robustness of the detection, using the labels polluted by the noise data from non-experts [6]}.
Another ways is to use a weakly supervised framework for classification and localization of lesions [7]}. Yet, it is still difficult for these methods to identify the boundaries of GGO as a result of its low contrast and blurred appearances.
